Testing Gaussian Process Surrogates on CEC’2013 Multi-Modal Benchmark

0462910 - ÚI 2017 RIV DE eng C - Konferenční příspěvek (zahraniční konf.)
Orekhov, N. - Bajer, L. - Holeňa, Martin
Testing Gaussian Process Surrogates on CEC’2013 Multi-Modal Benchmark.
Proceedings ITAT 2016: Information Technologies - Applications and Theory. Aachen & Charleston: Technical University & CreateSpace Independent Publishing Platform, 2016 - (Brejová, B.), s. 138-146. CEUR Workshop Proceedings, V-1649. ISBN 978-1-5370-1674-0. ISSN 1613-0073.
[ITAT 2016. Conference on Theory and Practice of Information Technologies /16./. Tatranské Matliare (SK), 15.09.2016-19.09.2016]
Grant ostatní: GA MŠk(CZ) LM2015042
Institucionální podpora: RVO:67985807
Klíčová slova: Gaussian process * ordinary regression * surrogate modelling * black-box optimization * CMA-ES Gaussian process * ordinary regression * surrogate modelling * black-box optimization * CMA-ES
Kód oboru RIV: IN - Informatika
http://ceur-ws.org/Vol-1649/138.pdf

This paper compares several Gaussian-processbased surrogate modeling methods applied to black-box optimization by means of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), which is considered state-of-the-art in the area of continuous black-box optimization. Among the compared methods are the Modelassisted CMA-ES, the Robust Kriging Metamodel CMAES, and the Surrogate CMA-ES. In addition, a very successful surrogate-assisted self-adaptive CMA-ES, which is not based on Gaussian processes, but on ordinary regression by means of support vector machines has been included into the comparison. Those methods have been benchmarked using CEC’2013 testing functions. We show that the surrogate CMA-ES achieves best results at the beginning and later phases of optimization process, conceding in the middle to surrogate-assisted CMA-ES.
Trvalý link: http://hdl.handle.net/11104/0262257